scholarly journals Landform classification using GIS techniques. The case of Kimi municipality area, Euboea Island, Greece.

2016 ◽  
Vol 47 (1) ◽  
pp. 264 ◽  
Author(s):  
I. Ilia ◽  
D. Rozos ◽  
I. Koumantakis
Keyword(s):  
Spatial Analysis ◽  
Power Index ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Wetness Index ◽  
Topographic Position Index ◽  
Landform Classification ◽  
Essential Parameter ◽  
Position Index ◽  
Stream Power Index

The main objective of this paper is to classify landforms in Kimi municipality area of Euboea Island, Greece using advanced spatial techniques. Landform categories were determined by conducting morphometric analysis through the use of advanced GIS functions. In particular, the process of classifying the landscape into landform categories was based on Topographic Position Index (TPI). The main topographic elements such as slope inclination, aspect, slope shape (curvature), topographic wetness index and stream power index were obtained from the DEM file of the study area. Landform classification was obtained using TPI grids and the classes were related with the geological pattern and the land cover by sophisticated spatial analysis function. The knowledge obtained from the present study could be useful in identifying areas prone to land degradation and instability problems in which landforms are identified as an essential parameter

scholarly journals Landslide susceptibility analysis using decision tree method, Phidim, Eastern Nepal

2013 ◽  
Vol 15 ◽  
pp. 69-76 ◽  
Author(s):  
Chandra Prakash Poudyal
Keyword(s):  
Decision Tree ◽  
Landslide Susceptibility ◽  
Power Index ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Effective Parameters ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Landslide Susceptibility Map ◽  
Stream Power Index

The decision tree is one of the new methods used for the determination of landslide susceptibility in the study area. The Phidim area is selected for the application of this method. The total surface area is 168.07 sq. km, and is located at the eastern part of Nepal. There are total of 10 different data bases used for this study which are; geological formation, elevation, slope, curvature, aspect, stream power index, topographic wetness index, distance from drainage, lineaments, and slope length, and are considered as landslide conditioning factors. Geographical information system (GIS) is used as basic tools and ARC/View is used for the processing data analysis and final map preparation. For the decision tree analysis the PASW 18 (statistical tool) is used to generate values of each factor. According to the results of decision tree, two geological formations; stream power index and slope are found as the most effective parameters on the landslide occurrence in the study area. Using the predicted values, the landslide susceptibility map of the study area is produced. To assess the performance of the produced susceptibility map, the area under curve (AUC) is drawn. The AUC value of the produced landslide susceptibility map has been obtained as 95.9%. According to the results of the AUC evaluation, the produced map has showed a good performance. As to wrap up, the produced map is able to be used for medium scaled and regional planning purposes. DOI: http://dx.doi.org/10.3126/bdg.v15i0.7419 Bulletin of the Department of Geology, Vol. 15, 2012, pp. 69-76

scholarly journals Harris Hawks Optimization: A Novel Swarm Intelligence Technique for Spatial Assessment of Landslide Susceptibility

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3590 ◽  
Author(s):  
Bui ◽  
Moayedi ◽  
Kalantar ◽  
Osouli ◽  
Gör ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Absolute Error ◽  
Slope Aspect ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Conditioning Factors ◽  
Performance Error ◽  
Stream Power Index ◽  
Artificial Neural Network Ann

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.

Geoinformation mapping of landscapes on the example of the Primorskiy Ridge (Baikal region)

2020 ◽  
Vol 954 (12) ◽  
pp. 20-30
Author(s):  
Yu.V. Vanteeva ◽  
Е.А. Rasputina ◽  
S.V. Solodyankina
Keyword(s):  
Digital Elevation Model ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Topographic Wetness Index ◽  
Space Images ◽  
Topographic Position Index ◽  
Digital Elevation ◽  
Different Seasons ◽  
Position Index ◽  
Elevation Model

The authors present the results of geoinformation mapping the Primorskiy Ridge landscapes using Landsat 8 satellite images, the digital elevation model SRTM and the factor-dynamic classification of geosystems. At the first stage, the remote sensing data for different seasons were classified using the ISODATA method. Then, using the digital elevation model, the landforms were classified basing upon the topographic position index. According to combining the classification parameters of one of the space images and digital elevation model, each polygon is automatically assigned to a certain preliminary type of landscapes using boolean expressions. Legend adjustments were made basing upon the fieldwork materials. As a result, a digital landscape map of the southern part of the Primorsky Ridge was created; it reflects the landscape structure at the level of facies groups and contains attributive information about the landform, altitude, slope and aspect, topographic wetness index. The analysis of the landscape pattern showed a high fragmentation of landscape polygons, formed due to overlay operations, which indicates the need for generalization of landscape contours.

scholarly journals Digital elevation models (DEM) used to assess soil erosion risks: a case study in Boyaca, Colombia

2016 ◽  
Vol 34 (2) ◽  
pp. 239-249 ◽  
Author(s):  
Jeiner Yobany Buitrago E. ◽  
Luis Joel Martínez M.
Keyword(s):  
Slope Aspect ◽  
Mountainous Area ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Alos Palsar ◽  
Erosion Risk ◽  
Digital Elevation ◽  
Stream Power Index ◽  
Logic Functions ◽  
Soil Surveys

The objective of this research was to develop a model for assessing the risk of erosion, exploring the potential of DEMs from SRTM, ASTER, ALOS PALSAR and one made with interpolation of a 1:25,000 contour map to calculate the variables of the relief that have greater impact on erosion. Several geomorphometric parameters, such as slope, aspect, profile and plan curvature, topographic wetness index, stream power index, and sediment transport capacity were computed from the DEM's elevation, some fuzzy logic functions proposed to evaluate the incidence of each parameter on erosion risk in a mountainous area of Colombia. The results showed that the use of DEM data is a relatively easy, uncostly method to identify, in a qualitative way, the risk of erosion and contribute to the enhancement of erosion information that is obtained with conventional general soil surveys.

scholarly journals Application of Fuzzy Best Worse Multi Criteria Decision Making Method for Flood Prioritization

2021 ◽  
Author(s):  
Sarita Gajbhiye Meshram ◽  
Ali reza Ildoromi ◽  
Mehdi Sepehri
Keyword(s):  
Decision Making ◽  
Water Conservation ◽  
Power Index ◽  
Hot Spot ◽  
Curve Number ◽  
Multi Criteria Decision Making ◽  
Stream Power ◽  
Stream Power Index ◽  
Flooded Areas

Abstract Flood is one of the major problems of the sad ekbatan watershed, northern of Hamadan province, Iran. This problem imposes high damages to the economic issue. Therefore, prioritization of the study area based on the flooding degree can be considered for identifying hot spot flooded areas for performing soil and water conservation practices. In this study, in order to prioritize sub-watersheds of the case study from viewpoint of flooding degree, five flood-related criteria i.e. entropy of drainage network (En), index of connectivity (IC), stream power index (SPI), curvature (C) and curve number (CN) were considered, then fuzzy based Best Worse Multi Criteria Decision Making (F-BWM) Method was used to assigning weights to used criteria and combination them to achieve flooding degree for each sub-watershed. The results of prioritization of sub-watersheds indicated that the sub-watersheds 14 and 21 are most and least susceptibility areas to flooding correspondingly.

scholarly journals Can Lumped Characteristics of a Contributing Area Provide Risk Definition of Sediment Flux?

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1787
Author(s):  
Barbora Jáchymová ◽  
Josef Krása ◽  
Tomáš Dostál ◽  
Miroslav Bauer
Keyword(s):  
Czech Republic ◽  
Large Scale ◽  
Power Index ◽  
Arable Land ◽  
Sediment Flux ◽  
Residential Areas ◽  
Stream Power ◽  
Watershed Area ◽  
Risk Definition ◽  
Stream Power Index

Accelerated soil erosion by water has many offsite impacts on the municipal infrastructure. This paper discusses how to easily detect potential risk points around municipalities by simple spatial analysis using GIS. In the Czech Republic, the WaTEM/SEDEM model is verified and used in large scale studies to assess sediment transports. Instead of computing actual sediment transports in river systems, WaTEM/SEDEM has been innovatively used in high spatial detail to define indices of sediment flux from small contributing areas. Such an approach has allowed for the modeling of sediment fluxes in contributing areas with above 127,484 risk points, covering the entire Czech Republic territory. Risk points are defined as outlets of contributing areas larger than 1 ha, wherein the surface runoff goes into residential areas or vulnerable bodies of water. Sediment flux indices were calibrated by conducting terrain surveys in 4 large watersheds and splitting the risk points into 5 groups defined by the intensity of sediment transport threat. The best sediment flux index resulted from the correlation between the modeled total sediment input in a 100 m buffer zone of the risk point and the field survey data (R2 from 0.57 to 0.91 for the calibration watersheds). Correlation analysis and principal component analysis (PCA) of the modeled indices and their relation to 11 lumped characteristics of the contributing areas were computed (average K-factor; average R-factor; average slope; area of arable land; area of forest; area of grassland; total watershed area; average planar curvature; average profile curvature; specific width; stream power index). The comparison showed that for risk definition the most important is a combination of morphometric characteristics (specific width and stream power index), followed by watershed area, proportion of grassland, soil erodibility, and rain erosivity (described by PC2).

GIS-based MCDA for Gully Vulnerability Mapping Using AHP Techniques

2020 ◽  
Vol 4 (2) ◽  
pp. 45-63
Author(s):  
Ishaku Bashir ◽  
Rachel Sallau ◽  
Abubakar Sheikh ◽  
Zuni Aminu ◽  
Shu’aib Hassan
Keyword(s):  
Vulnerability Index ◽  
Drainage Density ◽  
Disaster Mitigation ◽  
Vulnerability Mapping ◽  
Slope Aspect ◽  
Land Area ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Vulnerability Class ◽  
Stream Power Index

This paper explores the potentiality of GIS-based Multi-Criteria Decision Analysis (MCDA) and Analytical Hierarchy Process (AHP) for gully vulnerability mapping. Multilayer information of basin characteristics, such as drainage density, Topographic Wetness Index (TWI), Stream Power Index (SPI), slope aspect and land use land cover (LULC), were used in this study to develop a Gully Vulnerability Index (GVI). A weighted approach was implemented on each criterion relative to their inferred influence on gully vulnerability and validated by determining the Consistency Ratio (CR). Findings show a varying magnitude of gully vulnerability across the study area. The low to medium gully vulnerability class was dominant covering a land area of 6557ha (21.25%), and mostly confined to developed areas. Still, it is noteworthy to observe that the severe gully vulnerability class covers a substantial land area of 5825ha (18.88%), which presents a great risk to infrastructural development and human settlements in the study area. The study has a model predictive capability with accuracy rate of 84.62%. The integration of the MCDA and AHP into GIS workflow is an effective approach critical to minimize the limitations associated with gully occurrence analysis, using a singular basin characteristic. The results obtained in the study will equally be important in determining gully risk zones, circumspect urban development, tracking and proper infrastructure construction plans for long-term gully disaster mitigation.

scholarly journals GIS-based Landform Classification of Eneoli thic Archaeological Sites in the Plateau-plain Transition Zone (NE Romania): Habitation Practices vs. Flood Hazard Perception

2019 ◽  
Vol 11 (8) ◽  
pp. 915 ◽  
Author(s):  
Mihu-Pintilie ◽  
Nicu
Keyword(s):  
Transition Zone ◽  
Large Scale ◽  
Flood Hazard ◽  
Predictive Modelling ◽  
Archaeological Site ◽  
Morphological Data ◽  
Hazard Perception ◽  
Topographic Position Index ◽  
Landform Classification ◽  
Position Index

The landforms of the Earth’s surface ranging from large-scale features to local topography are factors that influence human behavior in terms of habitation practices. The ability to extract geomorphological settings using geoinformatic techniques is an important aspect of any environmental analysis and archaeological landscape approach. Morphological data derived from DEMs with high accuracies (e.g., LiDAR data), can provide valuable information related to landscape modelling and landform classification processes. This study applies the first landform classification and flood hazard vulnerability of 730 Eneolithic (ca. 5000–3500 BCE) settlement locations within the plateau-plain transition zone of NE Romania. The classification was done using the SD (standard deviation) of TPI (Topographic Position Index) for the mean elevation (DEV) around each archaeological site, and HEC-RAS flood hazard pattern generated for 0.1% (1000 year) discharge insurance. The results indicate that prehistoric communities preferred to place their settlements for defensive purposes on hilltops, or in the close proximity of a steep slope. Based on flood hazard pattern, 8.2% out of the total sites had been placed in highly vulnerable areas. The results indicate an eco-cultural niche connected with habitation practices and flood hazard perception during the Eneolithic period in the plateau-plain transition zone of NE Romania and contribute to archaeological predictive modelling.

scholarly journals Optimal Combination of Predictors and Algorithms for Forest Above-Ground Biomass Mapping from Sentinel and SRTM Data

2019 ◽  
Vol 11 (4) ◽  
pp. 414 ◽  
Author(s):  
Lin Chen ◽  
Yeqiao Wang ◽  
Chunying Ren ◽  
Bai Zhang ◽  
Zongming Wang
Keyword(s):  
Vegetation Indices ◽  
Absolute Error ◽  
Optimal Combination ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Stream Power ◽  
Topographic Wetness Index ◽  
Ground Biomass ◽  
Shuttle Radar Topographic Mission ◽  
Stream Power Index

Accurate forest above-ground biomass (AGB) mapping is crucial for sustaining forest management and carbon cycle tracking. The Shuttle Radar Topographic Mission (SRTM) and Sentinel satellite series offer opportunities for forest AGB monitoring. In this study, predictors filtered from 121 variables from Sentinel-1 synthetic aperture radar (SAR), Sentinal-2 multispectral instrument (MSI) and SRTM digital elevation model (DEM) data were composed into four groups and evaluated for their effectiveness in prediction of AGB. Five evaluated algorithms include linear regression such as stepwise regression (SWR) and geographically weighted regression (GWR); machine learning (ML) such as artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that the RF model used predictors from both the Sentinel series and SRTM DEM performed the best, based on the independent validation set. The RF model achieved accuracy with the mean error, mean absolute error, root mean square error, and correlation coefficient in 1.39, 25.48, 61.11 Mg·ha−1 and 0.9769, respectively. Texture characteristics, reflectance, vegetation indices, elevation, stream power index, topographic wetness index and surface roughness were recommended predictors for AGB prediction. Predictor variables were more important than algorithms for improving the accuracy of AGB estimates. The study demonstrated encouraging results in the optimal combination of predictors and algorithms for forest AGB mapping, using openly accessible and fine-resolution data based on RF algorithms.

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